December 2024 arXiv papers — page 200

Inmaculada Baldomá, Marcel Guardia, Dmitry E. Pelinovsky

Exponential small splitting of separatrices in the singular perturbation theory leads generally to nonvanishing oscillations near a saddle--center point and to nonexistence of a true homoclinic orbit. It was conjectured long ago that the oscillations may vanish at a countable set of small parameter values if there exist a quadruplet of singularities in the c

Miroslav Purkrabek, Jiri Matas

Human pose estimation methods work well on isolated people but struggle with multiple-bodies-in-proximity scenarios. Previous work has addressed this problem by conditioning pose estimation by detected bounding boxes or keypoints, but overlooked instance masks. We propose to iteratively enforce mutual consistency of bounding boxes, instance masks, and poses.

Martin Emons, Samuel Gunz, Helena L. Crowell, Izaskun Mallona

Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patte

Abdel Alheyasat, Gabriel Torrens, Sebastia A. Bota, Bartomeu Alorda

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment during the power-up process, they are subject to low challenge-response repeatability. The dependability of SRAM-PUFs is us

Bo Ji, Angela Yao

State-of-the-art video deblurring methods use deep network architectures to recover sharpened video frames. Blurring especially degrades high-frequency (HF) information, yet this aspect is often overlooked by recent models that focus more on enhancing architectural design. Recovering these fine details is challenging, partly due to the spectral bias of neura

Dhiman Paul, Md Rizwan Parvez, Nabeel Mohammed, Shafin Rahman

Prevailing joint prediction transformers for Video Highlight Detection and Moment Retrieval (HD/MR) exhibit deficiencies in handling cross-task dynamics, achieving robust video-text alignment, and utilizing effective attention mechanisms, with the potential of Large Language/Vision-Language Models (LLMs/LVLMs) being largely untapped. This paper introduces Vi

Chaolv Zeng, Yuan Tian, Guanjie Zheng, Yunjun Gao

Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. However, a critical aspect often overlooked by many existing methods is the explicit incorporation of \textbf{time-related features} (e.g., season, month, day of the week, hour, minute

Hao Tang, Zechao Li, Dong Zhang, Shengfeng He

RGB-Thermal Salient Object Detection aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities. Inspired by hierarchical

Michaela Brchnelova, Boris Gudiksen, Mats Carlsson, Andrea Lani

Space weather modelling has been gaining importance due to our increasing dependency on technology sensitive to space weather effects, such as satellite services, air traffic and power grids. Improving the reliability, accuracy and numerical performance of space weather modelling tools, including global coronal models, is essential to develop timely and accu

MD Shaikh Rahman, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid

Approximate Nearest Neighbor search is one of the keys to high-scale data retrieval performance in many applications. The work is a bridge between feature extraction and ANN indexing through fine-tuning a ResNet50 model with various ANN methods: FAISS and Annoy. We evaluate the systems with respect to indexing time, memory usage, query time, precision, recal

Andy Wathen

The solution of systems of linear(ized) equations lies at the heart of many problems in Scientific Computing. In particular for systems of large dimension, iterative methods are a primary approach. Stationary iterative methods are generally based on a matrix splitting, whereas for polynomial iterative methods such as Krylov subspace iteration, the splitting

Bo Ji, Angela Yao

Standard 3D Gaussian Splatting (3DGS) relies on known or pre-computed camera poses and a sparse point cloud, obtained from structure-from-motion (SfM) preprocessing, to initialize and grow 3D Gaussians. We propose a novel SfM-Free 3DGS (SFGS) method for video input, eliminating the need for known camera poses and SfM preprocessing. Our approach introduces a

Xingyu Liu, Gu Wang, Chengxi Li, Yingyue Li

We present GFreeDet, an unseen object detection approach that leverages Gaussian splatting and vision Foundation models under model-free setting. Unlike existing methods that rely on predefined CAD templates, GFreeDet reconstructs objects directly from reference videos using Gaussian splatting, enabling robust detection of novel objects without prior 3D mode

Alice Gasparini, Andreas Mueller, Florian Stern, Laura Weiss

Cosmology and GR remain largely inaccessible to high-school teaching due to the advanced prerequisites to master these topics. Integrating them into upper secondary teaching is a significant challenge that remains unresolved. This contribution reports on an implementation study of a GR and cosmology course for upper secondary school students as part of an ed

Chunlin Yu, Hanqing Wang, Ye Shi, Haoyang Luo

3D affordance segmentation aims to link human instructions to touchable regions of 3D objects for embodied manipulations. Existing efforts typically adhere to single-object, single-affordance paradigms, where each affordance type or explicit instruction strictly corresponds to a specific affordance region and are unable to handle long-horizon tasks. Such a p

Adem Chanie Ali, Seid Muhie Yimam, Abinew Ali Ayele, Chris Biemann

This study examines the complex relationship between social media, polarization, and conflict, with a focus on digital peacebuilding and women's participation, using the Northern Ethiopia War as a case study. Using a qualitative exploratory design through in-depth interviews, focus groups, and document analysis, the research examines how social media platfor

ATLAS Collaboration

A measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb$^{-1}$ of proton-proton collisions at $\sqrt{s}=13$ TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is perform

Erick Galinkin, Martin Sablotny

The adoption of large language models (LLMs) in many applications, from customer service chat bots and software development assistants to more capable agentic systems necessitates research into how to secure these systems. Attacks like prompt injection and jailbreaking attempt to elicit responses and actions from these models that are not compliant with the

Samuel Ssekajja

In this note, we show that a lightlike hypersurface of an indefinite Sasakian manifold, which is tangent to structure vector field is not locally symmetric, semi-symmetric or semi-parallel.

Yubin Huang, Jean Spiece, Tetiana Parker, Asaph Lee

The high electrical conductivity and good chemical stability of MXenes offer hopes for their use in many applications, such as wearable electronics, energy storage, or electromagnetic interference shielding. While their optical, electronic and electrochemical properties have been widely studied, the information on thermal properties of MXenes is scarce. In t

Kyriel Abad, Martin Henz

Computer science education has been at the heart of Scheme from the beginning. The language was designed in the 1970s concurrently with the MIT course 6.001 and the textbook "Structure and Interpretation of Computer Programs" (SICP). To explain the scope of variables at run time in the presence of higher-order procedures, SICP introduces a mental model calle

Håkan Andréasson, Markus Kunze, Gerhard Rein

We give a new proof for the existence of spherically symmetric steady states to the Vlasov-Poisson system, following a strategy that has been used successfully to approximate axially symmetric solutions numerically, both to the Vlasov-Poisson system and to the Einstein-Vlasov system. There are several reasons why a mathematical analysis of this numerical sch

Yufeng Jin, Vignesh Prasad, Snehal Jauhri, Mathias Franzius

Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics. While research in model-based 6D object pose estimation has delivered promising results, model-free methods are hindered by the high computational load in rendering and inferring c

Erick Galinkin, Emmanouil Pountrourakis, Spiros Mancoridis

With computing now ubiquitous across government, industry, and education, cybersecurity has become a critical component for every organization on the planet. Due to this ubiquity of computing, cyber threats have continued to grow year over year, leading to labor shortages and a skills gap in cybersecurity. As a result, many cybersecurity product vendors and

Nikolaos Chandrinos, Iliana Loi, Panagiotis Zachos, Ioannis Symeonidis

Artificial intelligence, machine learning, and deep learning as a service have become the status quo for many industries, leading to the widespread deployment of models that handle sensitive data. Well-performing models, the industry seeks, usually rely on a large volume of training data. However, the use of such data raises serious privacy concerns due to t

Marcel Wild

Various algorithms have been proposed to enumerate all connected induced subgraphs of a graph $G=(V,E)$. As a variation we enumerate all "packings of connected sets", i.e. partitions $\Pi$ of $V$ with the property that each part of $\Pi$ induces a connected subgraph. More generally, for various types $T$ of graphs we do (one or both of) the following: (i) en

Christina Kassab, Matías Mattamala, Sacha Morin, Martin Büchner

3D open-vocabulary scene graph methods are a promising map representation for embodied agents, however many current approaches are computationally expensive. In this paper, we reexamine the critical design choices established in previous works to optimize both efficiency and performance. We propose a general scene graph framework and conduct three studies th

Riccardo Travaglino, Colin Rylands, Pasquale Calabrese

We employ the quasiparticle picture of entanglement evolution to obtain an effective description for the out-of-equilibrium Entanglement Hamiltonian at the hydrodynamical scale following quantum quenches in free fermionic systems in two or more spatial dimensions. Specifically, we begin by applying dimensional reduction techniques in cases where the geometry

Xingyu Chen, Zhuheng Song, Xiaoke Jiang, Yaoqing Hu

Existing approaches of hand reconstruction predominantly adhere to a multi-stage framework, encompassing detection, left-right classification, and pose estimation. This paradigm induces redundant computation and cumulative errors. In this work, we propose HandOS, an end-to-end framework for 3D hand reconstruction. Our central motivation lies in leveraging a

Inés Pastor-Marazuela

Fast Radio Bursts (FRBs) are extragalactic millisecond-duration radio transients whose nature remains unknown. The advent of numerous facilities conducting dedicated FRB searches has dramatically revolutionised the field: hundreds of new bursts have been detected, and some are now known to repeat. Using interferometry, it is now possible to localise FRBs to

C. Alexandrou, S. Bacchio, J. Finkenrath, C. Iona

We determine the nucleon axial, scalar and tensor charges and the nucleon $\sigma$-terms using twisted mass fermions. We employ three ensembles with approximately equal physical volume of about 5.5~fm, three values of the lattice spacing, approximately 0.06~fm, 0.07~fm and 0.08~fm, and with the mass of the degenerate up and down, strange and charm quarks tun

Osama A. Marzouk

This study explores the suitability of hydrogen-based plasma in direct power extraction (DPE) as a non-conventional electricity generation method. We apply computational modeling and principles in physics and chemistry to estimate different thermal and electric properties of a water-vapor/nitrogen/cesium-vapor (H2O/N2/Cs) gas mixture with different levels of

Philippe Destuynder, Erwan Liberge

In an optimal control strategy, an important point is to define the cost of the control. Usually it is added to the control criterion and multiplied by a small coefficient denoted by $\varepsilon$ which is known as the marginal cost of the control. The key idea of this paper, is to introduce a smoothing term in the control cost which aims at reducing the qua

Michael Haylock, Martin Karlsson, Maksym Obrizan

Economic growth in Sweden during the early 20th Century was largely driven by industry. A significant contributor to this growth was the installation of different kinds of engines used to power factories. We use newly digitized data on engines and their energy source by industry sector, and combine this with municipality-level data of workers per industry se

Swapna Krishnakumar Radha, Andrey Kuehlkamp, Jarek Nabrzyski

Attestation of documents like legal papers, professional qualifications, medical records, and commercial documents is crucial in global transactions, ensuring their authenticity, integrity, and trustworthiness. Companies expanding operations internationally need to submit attested financial statements and incorporation documents to foreign governments or bus

Anthony Gibbons, Emma King, Ian Donohue, Andrew Parnell

Obtaining data to train robust artificial intelligence (AI)-based models for species classification can be challenging, particularly for rare species. Data augmentation can boost classification accuracy by increasing the diversity of training data and is cheaper to obtain than expert-labelled data. However, many classic image-based augmentation techniques ar

Sutirtha Datta, Navnath Daundkar, Abhishek Sarkar

We study the higher (sequential) topological complexity, a numerical homotopy invariant for the planar polygon spaces. For these spaces with a small genetic codes and dimension $m$, Davis showed that their topological complexity is either $2m$ or $2m+1$. We extend these bounds to the setting of higher topological complexity. In particular, when $m$ is power

Zhixiang Guo, Siyuan Liang, Aishan Liu, Dacheng Tao

Diffusion models have attracted significant attention due to its exceptional data generation capabilities in fields such as image synthesis. However, recent studies have shown that diffusion models are vulnerable to copyright infringement attacks, where attackers inject strategically modified non-infringing images into the training set, inducing the model to

Jens Bayer, Stefan Becker, David Münch, Michael Arens

Despite ongoing research on the topic of adversarial examples in deep learning for computer vision, some fundamentals of the nature of these attacks remain unclear. As the manifold hypothesis posits, high-dimensional data tends to be part of a low-dimensional manifold. To verify the thesis with adversarial patches, this paper provides an analysis of a set of

Jeremy S. Bradbury, Riddhi More

The use of large language models (LLMs) is widespread across many domains, including Software Engineering, where they have been used to automate tasks such as program generation and test classification. As LLM-based methods continue to evolve, it is important that we define clear and robust methods that fairly evaluate performance. Benchmarks are a common ap

Qian Shao, Bang Du, Yixuan Wu, Zepeng Li

Deep learning models for pulmonary disease screening from Computed Tomography (CT) scans promise to alleviate the immense workload on radiologists. Still, their high computational cost, stemming from processing entire 3D volumes, remains a major barrier to widespread clinical adoption. Current sub-sampling techniques often compromise diagnostic integrity by

Yujie Wang, Shiju Wang, Shenhan Zhu, Fangcheng Fu

Extending the context length (i.e., the maximum supported sequence length) of LLMs is of paramount significance. To facilitate long context training of LLMs, sequence parallelism has emerged as an essential technique, which scatters each input sequence across multiple devices and necessitates communication to process the sequence. In essence, existing sequen

Xi Guo, Chenjing Ding, Haoxuan Dou, Xin Zhang

Autonomous driving systems struggle with complex scenarios due to limited access to diverse, extensive, and out-of-distribution driving data which are critical for safe navigation. World models offer a promising solution to this challenge; however, current driving world models are constrained by short time windows and limited scenario diversity. To bridge th

J. Gamboa

The confinement of quarks is analyzed by establishing a correspondence between the effective actions inside a black hole and the QCD action, formulated as a scalar field theory in the Carrollian regime. We first demonstrate that both QCD (in the instanton-dominance limit) and the interior of a black hole can be described at one-loop as the effective action o

Abdelhady Naguib

This paper proposes a framework that has the ability to animate and generate different scenarios for the mobility of a movable anchor which can follow various paths in wireless sensor networks (WSNs). When the researchers use NS-2 to simulate a single anchor-assisted localization model, they face the problem of creating the movement file of the movable ancho

Eyvind Elstad, Harald Eriksen

The emergence of free AI tools like ChatGPT holds significant implications for developing writing skills in secondary education. This study examines AI's impact on students' writing competence and personal voice, balancing technological benefits against risks of dependency and plagiarism. We review the pros and cons of AI in the writing process, emphasizing

Tomer Borreda, Daniel Freedman, Or Litany

We present ReHub, a novel graph transformer architecture that achieves linear complexity through an efficient reassignment technique between nodes and virtual nodes. Graph transformers have become increasingly important in graph learning for their ability to utilize long-range node communication explicitly, addressing limitations such as oversmoothing and ov

Yiyue Zhang, Xin Jin, ZeYu Li, Kunya Yang

In the ferrimagnetic semiconductor Mn$_3$Si$_2$Te$_6$, a colossal magnetoresistance (CMR) is observed only when a magnetic field is applied along the magnetic hard axis ($\mathbf{H}\parallel c$). This phenomenon suggests an unconventional CMR mechanism potentially driven by the interplay between magnetism, topological band structure, and/or chiral orbital cu

Eyvind Elstad

This study examines the integration of generative AI in schools, assessing its benefits and risks. As AI use by students grows, it's crucial to understand its impact on learning and teaching practices. Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role. The article differentiates large language models f

F. Theel, M. Bonkhoff, P. Schmelcher, T. Posske

Chiral symmetry is broken by typical interactions in lattice models, but the statistical interactions embodied in the anyon-Hubbard model are an exception. This is an example for a correlated hopping model where chiral symmetry protects a degenerate zero-energy subspace. Complementary to the traditional approach of anyon braiding in real space, we adiabatica

Rachid Semmami, Hamid Ezzahraoui

The purpose of this paper is to present a new class of operators known as polynomially hypo-EP operators, extending the notation of hypo-EP, $n$-hypo-EP, and polynomially EP. The paper explores numerous properties and characterizations of polynomially hypo-EP operators and polynomially EP operators. In addition, we characterize polynomially hypo-EP operators

Silvia Vernetto, Carlo Francesco Vigorito, Martin Kakona, Roman Dvorak

This paper describes the performance of the LABDOS01, a silicon diode-based spectrometer suitable for dose measurements in mixed radiation fields. The instrument is currently being used in two high-altitude environmental dose monitoring projects: SAMADHA (South Atlantic Magnetic Anomaly Dosimetry at High Altitude) at Chacaltaya (Bolivia, 5240 m a.s.l.) and C

Jakub Kuzilek, Mustafa Çavuş

This study explores how the Rashomon effect influences variable importance in the context of student demographics used for academic outcomes prediction. Our research follows the way machine learning algorithms are employed in Educational Data Mining, focusing on highlighting the so-called Rashomon effect. The study uses the Rashomon set of simple-yet-accurat

Matthias Hamann, Karl Heuer

In this paper we define a degree for ends of infinite digraphs. The well-definedness of our definition in particular resolves a problem by Zuther. Furthermore, we extend our notion of end degree to also respect, among others, the vertices dominating the end, which we denote as combined end degree. Our main result is a characterisation of the combined end deg

Yuchen Zhu, Molei Tao, Yuebo Jin, Xie Chen

In quantum many-body systems, measurements can induce qualitative new features, but their simulation is hindered by the exponential complexity involved in sampling the measurement results. We propose to use machine learning to assist the simulation of measurement-induced quantum phenomena. In particular, we focus on the measurement-altered quantum criticalit

Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle, Golnaz Shahtahmassebi

Recently, the quality of artworks generated using Artificial Intelligence (AI) has increased significantly, resulting in growing difficulties in detecting synthetic artworks. However, limited studies have been conducted on identifying the authenticity of synthetic artworks and their source. This paper introduces AI-ArtBench, a dataset featuring 185,015 artis

Xiaoqi Liu, Pablo Pascual Cobo, Ramji Venkataramanan

We study the Gaussian multiple access channel with random user activity, in the regime where the number of users is proportional to the code length. The receiver may know some statistics about the number of active users, but does not know the exact number nor the identities of the active users. We derive two achievability bounds on the probabilities of misse

Mikolaj Fraczyk, Ben Lowe

We study the higher expansion properties of locally symmetric spaces, with a particular focus on octonionic hyperbolic manifolds. We show that codimension two minimal submanifolds of compact octonionic locally symmetric spaces must have large volume, at least linear in the volume of the ambient space. As a corollary we prove linear waist inequalities for oct

Jan Richter, Moto Togawa, José R. Crespo López-Urrutia, Andrey Surzhykov

Accurate lifetime measurements of excited states of highly charged ions (HCIs) are essential for advancing diagnostics in both laboratory and astrophysical plasmas, especially in the X-ray regime. The Hanle effect, which utilizes external magnetic fields to modify photon scattering patterns, provides a powerful technique for these measurements. Previously, t

Anton Nuzhdin, Alexander Nagaev, Alexander Sautin, Alexander Kapitanov

This paper proposes the second version of the widespread Hand Gesture Recognition dataset HaGRID -- HaGRIDv2. We cover 15 new gestures with conversation and control functions, including two-handed ones. Building on the foundational concepts proposed by HaGRID's authors, we implemented the dynamic gesture recognition algorithm and further enhanced it by addin

Adrian J. Bevan, Fani E. Taifakou, Choudhry Z. Amjad, Aled Horner

In recent decades organic electronics has entered the mainstream of consumer electronics, driven by innovations in scalability and low power applications, and low-cost fabrication methods. The potential for using organic semiconductor electronic devices as radiation detectors, and in particular for neutron detection is reported. We report results of laborato

A. V. Eskin, V. I. Korobov, A. P. Martynenko, F. A. Martynenko

We study the energy spectrum of three-particle systems (He-p-\mu), (He-d-\mu), (Li-p-\mu) and (Li-d-\mu) on the basis of variational approach with exponential and Gaussian basis. Using the Complex Coordinate Rotation (CCR) method we calculate energies of resonant states of listed molecules.

Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng

We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features

Xian Shuai, Yiding Wang, Yimeng Wu, Xin Jiang

Large language models (LLMs) have made remarkable advances in recent years, with scaling laws playing a critical role in this rapid progress. In this paper, we empirically investigate how a critical hyper-parameter, i.e., the global batch size, influences the LLM training prdocess. We begin by training language models ranging from 125 million to 2.6 billion

Emmanuelle Bourigault, Amir Jamaludin, Andrew Zisserman

Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, extracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, we propose an automated genera

Xiaoxue Ran, Sylvain Capponi, Junchen Rong, Fabien Alet

The quantum loop and dimer models are archetypal correlated systems with local constraints. With natural foundations in statistical mechanics, they are of direct relevance to various important physical concepts and systems, such as topological order, lattice gauge theories, geometric frustrations, or more recently Rydberg arrays quantum simulators. However,

Federico Librino, Paolo Santi

In this paper, we explore how radio frequency energy from vehicular communications can be exploited by an energy harvesting device (EHD) placed alongside the road to deliver data packets through wireless connection to a remote Access Point. Based on updated local topology knowledge, we propose a cycle-based strategy to balance harvest and transmit phases at

Lasantha Thakshila Wedage, Mehmet C. Vuran, Bernard Butler, Christos Argyropoulos

The proliferation of nano-technology has enabled novel applications in various fields, including the potential for miniaturized terahertz (THz)-enabled devices. The extra-large bandwidth available in the THz spectrum can facilitate high-speed communication, even for transmission through lossy media. Culminating these capabilities, this paper introduces a new

Yuxuan Zhou, Xingxing Li, Shengyu Li, Chunxi Xia

For high-level geo-spatial applications and intelligent robotics, accurate global pose information is of crucial importance. Map-aided localization is a universal approach to overcome the limitations of global navigation satellite system (GNSS) in challenging environments. However, current solutions face challenges in terms of mapping flexibility, storage bu

Lukas Danner, Florian Höhe, Ciprian Padurariu, Joachim Ankerhold

Bright sources of quantum microwave light are an important building block for various quantum technological applications. Josephson junctions coupled to microwave cavities are a particularly versatile and simple source for microwaves with quantum characteristics, such as different types of squeezing. Due to the inherent nonlinearity of the system, a pure dc-

Hanning Wang, Xiang Huang, Rong-Rong Chen, Arman Farhang

Delay-Doppler (DD) signal processing has emerged as a powerful tool for analyzing multipath and time-varying channel effects. Due to the inherent sparsity of the wireless channel in the DD domain, compressed sensing (CS) based techniques, such as orthogonal matching pursuit (OMP), are commonly used for channel estimation. However, many of these methods assum

Zhen Gong, Baiqiang Liu, Xinrui Yang, Hongbo Jing

Stabilizing nitrogen-rich compound crystals under conventional conditions is a key issue in the development and application of high-energy density materials (HEDMs). Herein, a two-dimensional double-layer interlocked Li4(N5)2 nitrogen-rich compound crystals, in which the two N5 rings are locked to by sharing four Li atoms, was found to maintain structural st

Nicholas Konz, Richard Osuala, Preeti Verma, Yuwen Chen

Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segm

Max van Spengler, Jan Zahálka, Pascal Mettes

As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyperbolic alternatives to the commonly used FGM and PGD adversarial attacks. Through interpretable synthetic benchmarks and experiments on existing datasets, we show how the existing a

Eliane Kummer, Stephan Simonis

Recently, the construction of finite difference schemes from lattice Boltzmann schemes has been rigorously analyzed [Bellotti et al. (2022), Numer. Math. 152, pp. 1-40]. It is thus known that any lattice Boltzmann scheme can be expressed in terms of a corresponding multi-step finite difference scheme on the conserved variables. In the present work, we provid

Qirui Yang, Peng-Tao Jiang, Hao Zhang, Jinwei Chen

Learning lighting adaptation is a crucial step in achieving good visual perception and supporting downstream vision tasks. Current research often addresses individual light-related challenges, such as high dynamic range imaging and exposure correction, in isolation. However, we identify shared fundamental properties across these tasks: i) different color cha

Rudra R. Kamat, Hemant K. Mishra

We establish necessary and sufficient conditions on simultaneous symplectic spectral decomposition of a family of $2n \times 2n$ real positive semidefinite matrices with symplectic kernels. We also provide a precise algebraic condition on a $2n \times 2n$ real positive semidefinite matrix with symplectic kernel for orthosymplectic spectral diagonalization, w

Koen Minartz, Fleur Hendriks, Simon Martinus Koop, Alessandro Corbetta

Understanding the dynamics of pedestrian crowds is an outstanding challenge crucial for designing efficient urban infrastructure and ensuring safe crowd management. To this end, both small-scale laboratory and large-scale real-world measurements have been used. However, these approaches respectively lack statistical resolution and parametric controllability,

Jialin Wang, Zhihua Duan

This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities and integrates with LangGraph for workflow orches

Hugo Malard, Michel Olvera, Stephane Lathuiliere, Slim Essid

Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications. Here, we tackle the specific task of sound-prompted segmentation, aiming to segment image regions corresponding to objects heard in an audio signal. Most existing approaches tackle this problem by fine-t

Gabriela Ben-Melech Stan, Estelle Aflalo, Man Luo, Shachar Rosenman

Large Vision Language Models (LVLMs) have demonstrated remarkable reasoning capabilities over textual and visual inputs. However, these models remain prone to generating misinformation. Identifying and mitigating ungrounded responses is crucial for developing trustworthy AI. Traditional explainability methods such as gradient-based relevancy maps, offer insi

Jonas Sauer, Scott A. Smith

In this expository note, we show that the blow-up arguments of L. Simon adapt well to the corresponding Schauder theory of germs used in the study of singular SPDEs. We illustrate this through some representative examples. As in the classical PDE framework, the argument relies only on the scaling properties of the germ semi-norms and the Liouville principle

Cong Xie, Han Zou, Ruiqi Yu, Yan Zhang

In this work, we are interested in achieving both high text controllability and whole-body appearance consistency in the generation of personalized human characters. We propose a novel framework, named SerialGen, which is a serial generation method consisting of two stages: first, a standardization stage that standardizes reference images, and then a persona

Andrzej Indrzejczak, Michał Zawidzki

We provide a version of first-order hybrid tense logic with predicate abstracts and definite descriptions as the only non-rigid terms. It is formalised by means of a tableau calculus working on sat-formulas. A particular theory of DD exploited here is essentially based on the approach of Russell, but with descriptions treated as genuine terms. However, the r

Romuald Kilianski, Claire M. Cisowski, Robert Bennett

We present a general-purpose algorithm for automatic production of a structure that induces a desired Casimir-Polder force. As a demonstration of the capability and wide applicability of the method, we use it to develop a geometry that leads to a repulsive Casimir-Polder force on a ground-state atom. The results turn out to be reminiscent of the ring-like ge

O. S. Pastoven, O. V. Kompaniiets, I. B. Vavilova, I. O. Izviekova

We studied the multiwavelength properties of NGC 3521, the Milky Way galaxy-twin, from UV- to radio, exploring the data from GALEX for UV-, SDSS for optical, 2MASS, WISE, MIPS (Spitzer) and PACS, SPIRE (Herschel) for IR-, and NRAO VLA for radio ranges. To obtain the spectral energy distribution (SED), we exploited the CIGALE software and constructed SEDs wit

Neil Dizon, Tuomo Valkonen

With a view on bilevel and PDE-constrained optimisation, we develop iterative estimates $\widetilde{F'}(x^k)$ of $F'(x^k)$ for composite functions $F :=J \circ S$, where $S$ is the solution mapping of the inner optimisation problem or PDE. The idea is to form a single-loop method by interweaving updates of the iterate $x^k$ by an outer optimisation method, w

Grzegorz Ficht, Sven Behnke

We introduce an analytic method for generating a parametric and constraint-aware kick for humanoid robots. The kick is split into four phases with trajectories stemming from equations of motion with constant acceleration. To make the motion execution physically feasible, the kick duration alters the step frequency. The generated kicks seamlessly integrate wi

Kevin M. Hickson, Jean-Christophe Loison

The kinetics of the reactions of nitrogen dioxide, NO$_2$, with atomic oxygen and atomic carbon in their ground triplet states ($^3$P) have been studied at room temperature and below using a supersonic flow (Laval nozzle) reactor. O($^3$P) and C($^3$P) atoms (hereafter O and C respectively) were created in-situ by the pulsed laser photolysis of the precursor

G. Bruni, L. Piro, Y. -P. Yang, E. Palazzi

We present the discovery of the fourth persistent radio source (PRS) associated with a fast radio burst (FRB). Following previous indications of a candidate PRS associated with FRB20240114A, we performed deep VLBA observations at 5 GHz to test the presence of a compact radio source within the uncertainty position of this FRB ($\pm$200 mas). We detect a compo

Nitish Mital, Simon Malzard, Richard Walters, Celso M. De Melo

Limited real-world data severely impacts model performance in many computer vision domains, particularly for samples that are underrepresented in training. Synthetically generated images are a promising solution, but 1) it remains unclear how to design synthetic training data to optimally improve model performance (e.g, whether and where to introduce more re

RuiZhe Jiang, Haotian Lei

Over-parameterized neural network models often lead to significant performance discrepancies between training and test sets, a phenomenon known as overfitting. To address this, researchers have proposed numerous regularization techniques tailored to various tasks and model architectures. In this paper, we introduce a simple perspective on overfitting: models

J. Haddad

The radial mean bodies of parameter $p>-1$ of a convex body $K \subseteq \mathbb R^n$ are radial sets introduced in [4] by Gardner and Zhang. They are known to be convex for $p\geq 0$. We prove that if $K \subseteq \mathbb R^2$ is a convex body, then its radial mean body of parameter $p$ is convex for every $p \in (-1,0)$.

Filipo Sharevski, Jennifer Vander Loop, Sarah Ferguson

This paper reports on a study exploring user experiences with suspicious emails and associated warnings when accessed through virtual reality (VR) headsets in realistic settings. A group of (n=20) Apple Vision Pro and another group of (n=20) Meta Quest 3 users were invited to sort through their own selection of Google mail suspicious emails through the VR he

A. Naimy, A. Slaoui, A. Ali, H. El Hadfi

Quantum metrology leverages quantum effects such as squeezing, entanglement, and other quantum correlations to boost precision in parameter estimation by saturating quantum Cramer Rao bound, which can be achieved by optimizing quantum Fisher information or Wigner-Yanase skew information. This work provides analytical expressions for quantum Fisher and skew i

A. Simonnin, V. Parmentier, J. P. Wardenier, G. Chauvin

Wind dynamics play a pivotal role in governing transport processes within planetary atmospheres, influencing atmospheric chemistry, cloud formation, and the overall energy budget. Understanding the strength and patterns of winds is crucial for comprehensive insights into the physics of ultra-hot Jupiter atmospheres. This study focuses on unraveling the wind

Sangbeom Lim, Seongchan Kim, Seungjun An, Seokju Cho

Current benchmarks for video segmentation are limited to annotating only salient objects (i.e., foreground instances). Despite their impressive architectural designs, previous works trained on these benchmarks have struggled to adapt to real-world scenarios. Thus, developing a new video segmentation dataset aimed at tracking multi-granularity segmentation ta

Tomáš Blažek, Peter Maták, Viktor Zaujec

This work presents an asymmetric dark matter model with relic density determined by the freeze-out of asymmetric semi-annihilations into long-lived particles slowly decaying into the Standard Model states. We carefully consider the $CPT$ symmetry and unitarity constraints to the asymmetries entering the Boltzmann equation. The main idea of the paper is to po

Yulin Wang, Z. L. Wang

In this study, we designed a spiral-shaped milli-reactor with a T-junction microchannel to generate digital droplets for studying and observing the digital freezing process of droplets. During the study of the recalescence and solidification processes of digital droplets dynamically moving in microchannels, we found that although the digital generation of dr

Junzhi Li, Jingliang Sun, Teng Long, Zhenlin Zhou

Due to the strong nonlinearity and nonholonomic dynamics, despite the various general trajectory optimization methods presented, few of them can guarantee efficient computation and physical feasibility for relatively complicated fixed-wing UAV dynamics. Aiming at this issue, this paper investigates a differential flatness-based trajectory optimization method